A Novel Global Threshold-Based Active Contour Model

Abstract

In this paper, we propose a novel global threshold-based active contour model which employs a new edge-stopping function that controls the direction of the evolution and stops the evolving contour at weak or blurred edges. The model is implemented using selective binary and Gaussian filtering regularized level set (SBGFRLS) method. The method has a selective local or global segmentation property. It selectively penalizes the level set function to be a binary function. This is followed by using a Gaussian function to regularize it. The Gaussian filters smooth the level set function and afford the evolution more stability. The contour could be initialized anywhere inside the image to extract object boundaries. The proposed method performs well when the intensities inside and outside the object are homogenous. Our method is tested on synthetic, medical and Arabiccharacters images with satisfactory results KEYWORDSImagesegmentation, Active contour,Geodesic active contour,C-V model,Level set method, ZAC model.

A Modified Distance Regularized Level Set Model for Liver Segmentation from CT Images

“…Methods that use texture properties work to simulate processes in human's brain. On the other hand, level set methods have been widely used in segmentation of medical images [25][26][27]. Despite of their wide and effective uses, successful application of level set relies heavily on the initial position.…”

Liver Segmentation from CT Images Using a Modified Distance Regularized Level Set Model Based on a Novel Balloon Force

“…Finally, texture based methods attempt to emulate the procedure that human's brain processes. Level set methods have been investigated and widely utilized in image segmentation especially for medical images segmentation [20][21][22].Current approaches in using level set methods represent promising approaches for segmenting irregular object shapes such as liver. However it has a strict requirement on the initial position.…”